Retrieval Augmented Generation (RAG) has been a cornerstone in enhancing large language models (LLMs) for complex, knowledge-driven tasks. By pulling in relevant data from a vector database, RAG has empowered LLMs with factual grounding, significantly reducing instances of fabricated information. But is this the end of the road for RAG?

Overcoming Core Limitations: A Glimpse into the Future

RAG’s necessity springs from fundamental constraints within current LLMs. These models, despite their impressive learning capabilities, derive their knowledge exclusively from pre-trained internet text, lacking in real-world awareness and struggling with factual reasoning. RAG, therefore, has been instrumental in injecting dynamic, external knowledge, enhancing the accuracy of LLMs in responding to knowledge-intensive prompts.

However, RAG isn’t without its flaws. The process of selecting appropriate embeddings, vector databases, and ranking algorithms is a delicate one, impacting the effectiveness of the results. In essence, RAG has been a fragile solution to a more deep-seated issue within LLMs.

The Dawn of New Architectures and Scaling